Air Quality Prediction in Smart Cities Using Regression Techniques

Air pollution in smart cities presents a significant risk to public health and general welfare. Precise air quality forecasting is essential for successful pollution reduction and long-term urban growth. This study performs a comprehensive comparative analysis of air quality prediction utilizing four regression techniques: Random Forest regression, Linear regression, Decision Tree regression, and XGBoost regression. The study seeks to determine the best effective model by evaluating criteria such as R2 measurements and Mean Absolute Error.The results demonstrate that the XGBoost regression method surpasses competing algorithms in terms of efficiency and accuracy. Moreover, the use of cloud computing technologies has greatly enhanced the implementation speed of these tactics. Utilizing distributed computing resources allows for real-time air quality forecasts, facilitating quick decision-making and proactive measures to combat air pollution incidents.This work enhances air quality prediction methods in smart cities by highlighting the effectiveness of the XGBoost regression algorithm. The study highlights the crucial need of utilizing advanced regression techniques and cloud computing to improve the precision and effectiveness of air quality forecasts, aiding in proactive efforts to address air pollution and create healthier urban settings.